Rare diseases affecting fewer than five in 10,000 people are often particularly difficult to diagnose and treat. As part of their joint funding program “zukunft.niedersachsen”, the state of Lower Saxony and the Volkswagen Foundation have now selected nine promising research projects for funding that aim to develop new diagnoses and treatments for rare diseases. Among them are two projects from the Helmholtz Centre for Infection Research (HZI). The PREDICT-CTP consortium of TWINCORE, the HZI, and Hannover Medical School (MHH) is developing a new diagnostic method for rheumatic diseases. In the StopPSC project, researchers from the HZI and MHH are developing new agents to treat primary sclerosing cholangitis, a chronically progressive inflammation of the bile ducts.

Experts are increasingly turning to machine learning to predict antibiotic resistance in pathogens. With its help, resistance mechanisms can be identified based on a pathogen’s genetics. However, the results should be viewed with caution: Researchers at the Helmholtz Institute for RNA-based Infection Research (HIRI) in Würzburg have shown that the models are often less reliable than assumed. Their findings were published in the journal PLOS Biology. They contribute to the development of more reliable tools for predicting and combating antibiotic resistance.

At first glance, biology and computer science seem like opposites. But wherever enormous amounts of data are generated from research, progress is hardly possible without digital methods. Bioinformatician Prof. Andreas Keller therefore relies on artificial intelligence. He heads the department “Clinical Bioinformatics” at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS). In this episode of InFact, he talks about how AI can help us understand how beneficial and harmful bacteria communicate with each other in our bodies, how to predict when infections will cause long-term effects, and how this can be used to develop new drugs against dangerous pathogens.